<h2>DESCRIPTION</h2>

<em>i.oif</em> calculates the Optimum Index Factor for 
multi-spectral satellite imagery.

<p>
The Optimum Index Factor (OIF) determines the three-band combination 
that maximizes the variability (information) in a multi-spectral 
scene. The index is a ratio of the total variance (standard 
deviation) within and the correlation between all possible band 
combinations. The bands that comprise the highest scoring 
combination from <em>i.oif</em> are used as the three color channels 
required for <em>d.rgb</em> or <em>r.composite</em>.

<p>The analysis is saved to a file in the current directory called "i.oif.result".


<h2>NOTES</h2>

Landsat 1-7 TM:
Colour Composites in BGR order as important Landsat TM band combinations
(example: 234 in BGR order means: B=2, G=3, R=4):

<ul>
<li> 123: near natural ("true") colour; however, because of 
correlation of the 3 bands in visible spectrum, this combination 
contains not much more info than is contained in single band.</li>
<li> 234: sensitive to green vegetation (portrayed as red), 
coniferous as distinctly darker red than deciduous forests. Roads 
and water bodies are clear.</li>
<li> 243: green vegetation is green but coniferous forests aren't as 
clear as the 234 combination.</li>
<li> 247: one of the best for info pertaining to forestry. Good for 
operation scale mapping of recent harvest areas and road 
construction.</li>
<li> 345: contains one band from each of the main reflective units 
(vis, nir, shortwave infra). Green vegetation is green and the 
shortwave band shows vegetational stress and mortality. Roads are 
less evident as band 3 is blue.</li>
<li> 347: similar to 345 but depicts burned areas better.</li>
<li> 354: appears more like a colour infrared photo.</li>
<li> 374: similar to 354.</li>
<li> 457: shows soil texture classes (clay, loam, sandy).</li>
</ul>

<p>
By default the module will calculate standard deviations for all bands in
parallel. To run serially use the <b>-s</b> flag. If the <tt>WORKERS</tt>
environment variable is set, the number of concurrent processes will be
limited to that number of jobs.


<h2>EXAMPLE</h2>

North Carolina sample dataset:
<div class="code"><pre>
g.region raster=lsat7_2002_10 -p
i.oif input=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70
</pre></div>


<h2>REFERENCES</h2>

Jensen, 1996. Introductory digital image processing. Prentice Hall, 
p.98. ISBN 0-13-205840-5


<h2>SEE ALSO</h2>

<em>
<a href="d.rgb.html">d.rgb</a>,
<a href="r.composite.html">r.composite</a>,
<a href="r.covar.html">r.covar</a>,
<a href="r.univar.html">r.univar</a>
</em>


<h2>AUTHORS</h2>

Markus Neteler, ITC-Irst, Trento, Italy<br>
Updated to GRASS 5.7 by Michael Barton, Arizona State University

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